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An artificial neural network (ANN) model was developed for predicting trichloroethylene (TCE) biodegradation via co-metabolism in a packed-bed biofilm reactor using Pseudomonas putida F1 as the bio-catalyst and toluene as the primary substrate. The model uses an architecture with three hidden layers (3–2–1) and resilient back-propagation algorithm to obtain optimal weights for the associated neurons. ANN model predictions of TCE degradation efficiency have not only successfully validated those obtained from a Response Surface Model (RSM), but the model also outperformed RSM with a better predicting power that the ANN showed a lower root mean square error (RMSE) 9.07 and a higher determination of coefficient (R²) 80.32 % than those of RSM (RMSE=9.93, 10.88 after corrected for model degree of freedom; R² = 76.4 %). Further model analysis revealed that this ANN model correctly interpreted the …